Research On The Compatible Aboveground Biomass Models Of Pinus Yunnanensis In Southwest China

Based on the measurement data of aboveground biomass of 1 50 Pinus ynunanensis in Sichuan,Yunnan and Tibet,the distribution of biomass in P·yunnanensis wood and leaves and other organs was analyzed.According to the relationship between the biomass of each part and the tree measuring factors,the explanatory variables of the model were determined,and 10 kinds of common models such as linear and logistic were selected to fit each part of the biomass,and the best model type was selected from it.On the foundation of the optimal model type,the general models for total above ground biomass as well as stem,wood,bark,crown,branch and leaf biomass for trees from different regions were established with basic biomass models and dummy variables characterized by geographic regions.Then,the compatible biomass models were built using the methods of nonlinear adjustment in proportion and nonlinear simultaneous equations with measurement error.According to different equations,the method was divided to direct control under total tree by proportions or sum control and so on.The results showed that:(1)The ratio of biomass to total biomass in different organs was not the same.The overall pattern was as followed:wood(55.14%)>branch(21.28%)>bark(12.45%)>leaf(11.00%).The proportion of wood biomass was positively correlated with tree age(DBH).The proportion of bark biomass and leaf biomass is negatively correlated with tree age(DBH),while the proportion of branch organisms was relatively stable throughout the life cycle.Moreover,there were also some differences in the biomass allocation of the same organs in Yunnan,Tibet and Sichuan.(2)The power model in the 10 common models was the,nost suitable for P·yunnanensis aboveground biomass and biomass of each item.Based on the Power model,the biomass independent regression models were satisfied,the trivariate models had the highest prediction accuracy,following by the bivariate models,and univariate lodels were the worst.In the bivariate models,the DaHb combination of breast height and tree height was better than that of D2H.And the optimization degree of each evaluation index of the one to two predictor variables biomass models were obviously greater than that from two to three predictor variables.After introducing dummy variables representing regions,the fitting coefficients of each models have been improved,this showed that introduction of dummy variable could integrate different regional biomass models effectively.(3)Both the nonlinear adjustment in proportion and nonlinear simultaneous equations with measurement error could efficiently ensure that the total biomass was equal to the summary of its components with high accuracy.Among the compatible models,the models with two predictor variables were better than models with one predictor variable.The fitting effect of the four schemes under the method of nonlinear simultaneous equation with measurement error were basically equal,but they were more accurate and more stable than the method of proportional adjustment.For balancing prediction accuracy and workload,it is suggested to adapt the nonlinear simultaneous equations with measurement error of sum control to build compatible biomass models.